• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.554-558
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• 2020

최근 사전학습 모델의 발달로 기계독해 시스템 성능이 크게 향상되었다. 하지만 기계독해 시스템은 주어진 단락에서 질문에 대한 정답을 찾기 때문에 단락을 직접 검색해야하는 실제 환경에서의 성능 하락은 불가피하다. 즉, 기계독해 시스템이 오픈 도메인 환경에서 높은 성능을 보이기 위해서는 높은 성능의 검색 모델이 필수적이다. 따라서 본 논문에서는 검색 모델의 성능을 보완해 줄 수 있는 오픈 도메인 기계독해를 위한 단락 재순위화 모델을 제안한다. 제안 모델은 합성곱 신경망을 이용하여 질문과 단락을 구절 단위로 표현했으며, N-gram 구절 사이의 상호 주의 집중을 통해 질문과 단락 사이의 관계를 효과적으로 표현했다. KorQuAD를 기반으로한 실험에서 제안모델은 MRR@10 기준 93.0%, Top@1 Precision 기준 89.4%의 높은 성능을 보였다.

• Neonatal Medicine
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• v.18 no.2
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• pp.272-279
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• 2011

Purpose: The incidence of nosocomial infection caused by Gram-negative bacilli (GNB) has increased in neonatal intensive care units (NICU). This study identified the progression of sepsis caused by GNB colonization and analyzed the risk factors associated with using periodic stool culture surveillance. Methods: We included 86 newborns admitted to the NICU, Kosin University Gospel Hospital from October 2007 to May 2008. Three stool specimens were collected right after birth and two more were collected at 2 week intervals. The risk factors related to GNB colonization were established from each medical record and related references. Results: The incidence of colonization by GNB was 22 (25.6%) per 86 neonates but none had culture-proven sepsis. The three most commonly isolated GNB were Pseudomonas aeruginosa, Enterobacter cloacae, and Citrobacter freundii. Approximately 89% (32/36) of isolated GNB were susceptible to amikacin. The probability of GNB colonization increased in infants who were fed a small volume during enteral feeding. In contrast, delayed enteral feeding resulted in a decreased probability for GNB colonization. Conclusion: Colonized GNB in the intestine was confirmed by enteric surveillance culture of newborns admitted to the NICU. However, we found no evidence of culture-proven GNB sepsis. As lower feeding volume on the colonization day is a risk factor for GNB colonization, the chance for GNB colonization should be considered when feeding intolerance is present.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.71-86
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• 2023

The purpose of this exploratory study is to compare the differences in public perceptual characteristics of Korean and Chinese societies regarding the metaverse using big data analysis. Due to the environmental impact of the COVID-19 pandemic, technological progress, and the expansion of new consumer bases such as generation Z and Alpha, the world's interest in the metaverse is drawing attention, and related academic studies have been also in full swing from 2021. In particular, Korea and China have emerged as major leading countries in the metaverse industry. It is a timely research question to discover the difference in social awareness using big data accumulated in both countries at a time when the amount of mentions on the metaverse has skyrocketed. The analysis technique identifies the importance of key words by analyzing word frequency, N-gram, and TF-IDF of clean data through text mining analysis, and analyzes the density and centrality of semantic networks to determine the strength of connection between words and their semantic relevance. Python 3.9 Anaconda data science platform 3 and Textom 6 versions were used, and UCINET 6.759 analysis and visualization were performed for semantic network analysis and structural CONCOR analysis. As a result, four blocks, each of which are similar word groups, were driven. These blocks represent different perspectives that reflect the types of social perceptions of the metaverse in both countries. Studies on the metaverse are increasing, but studies on comparative research approaches between countries from a cross-cultural aspect have not yet been conducted. At this point, as a preceding study, this study will be able to provide theoretical grounds and meaningful insights to future studies.